## General Information EditEdit

Introduction to probabilistic techniques for modeling random phenomena and making estimates, inferences, predictions, and engineering decisions in the presence of chance and uncertainty.

## Prerequisites EditEdit

MATH 2940, PHYS 2213, or equivalents.

## Topics Covered EditEdit

Probability measures, classical probability and combinatorics, countable and uncountable sample spaces, random variables, probability mass functions, probability density functions, cumulative distribution functions, important discrete and continuous distributions, functions of random variables including moments, independence and correlation, conditional probability, Total Probability and Bayes' rule with application to random system response to random signals, characteristic functions and sums of random variables, the multivariate Normal distribution, maximum likelihood and maximum a posteriori estimation, Neyman-Pearson and Bayesian statistical hypothesis testing, Monte Carlo simulation. Applications in communications, networking, circuit design, device modeling, and computer engineering.

## Workload EditEdit

Poitras went very fast with material, and wanted to cover the whole book. He taught straight from the book and besides 1 or 2 examples early in the semester, all lecture was exactly from book. Can self-teach the material if you just the read the book on your own time.

## Follow on Courses EditEdit

## Advice EditEdit

## Past Offerings EditEdit

Semester |
Time |
Professor |
Median Grade |
---|---|---|---|

Spring 2016 | TR 02:55 – 04:10 | Eilyan Bitar
| B+ |

Spring 2017 |
MWF 1:25-2:15 | Eilyan Bitar | B |